Multimodal Preference Aggregation for Multimedia Information Retrieval
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| Title | Multimodal Preference Aggregation for Multimedia Information Retrieval |
| Authors | |
| Abstract | Representing and fusing multimedia information is a key issue to discover semantics in multimedia. In this paper we address more specifically the problem of multimedia content retrieval through the joint design of an original multimodal information representation and of a machine learning-based fusion algorithm. We first define a novel preference-based representation particularly adapted to the retrieval problem, and then, we investigate the RankBoost algorithm to combine those preferences to fullfill a user’s query. Interestingly, it ends up being a flexible retrieval model that only manipulates ranking information and is blind to the intrinsic properties of the multimodal information input. The approach is tested on annotated images and on the complete TRECVID 2005 corpus and compared with SVM-based fusion strategies. The results show that our approach equals SVM performance but, contrary to SVM, is parameter free and faster. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2009-10-01 |
| Source | Journal of Multimedia Vol 4, No 5 (2009): Special Issue: Multimodal Multimedia Retrieval |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |